KNN with TF-IDF based Framework for Text Categorization
By Bruno Trstenjak, Sanja Mikac, Dzenana Donko. 2014
349 citations recorded by the source corpus.
Abstract KNN is a very popular algorithm for text classification. This paper presents the possibility of using KNN algorithm with TF-IDF method and framework for text classification. Framework enables classification according to various parameters, measurement and analysis of results. Evaluation of framework was focused on the speed and quality of classification. The results of testing showed the good and bad features of algorithm, providing guidance for the further development of similar frameworks.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/7054f4284fd3a67eed4d021928f4303d79fc7392
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In Artificial Intelligence
In Information Systems
Citations in the corpus
- Survey on supervised machine learning techniques for automatic text classification (2019, positive): “In addition, there are different functions that were used to find the similarity between the documents is normally calculated based on the distance between document pairs was given by Trstenjak et al. (2014) as shown in Table 1.”
- Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents (2018, background): “INTRODUCTION The processing of structured or semi—structured data in all organizations is becoming very difficult as the data has been increased tremendously [1], [2].”
- Comparing BERT against traditional machine learning text classification (2020, methodology): “In this work we compare BERT model [3] with a traditional ML NLP approach that trains ML algorithms in features retrieved by the Term Frequency - Inverse Document Frequency (TF-IDF) algorithm as a representative of these traditional approaches [11].”